How does Monte Carlo Exploring Starts work?
each episode you will calculate the return, you will then update the action value or $Q(s,a)$  as the average each episode. Using the blackjack example from open AI gym and using a discount factor of 1, you get the following  

episode 1 
[{'state': (22, 10, False), 'reward': -1, 'action': 1}, {'state': (17, 10, False), 'reward': 0, 'action': 1}, {'state': (12, 10, False), 'reward': 0.0, 'action': 1}]

$Q((22, 10, False),0)=-1$

$Q((17, 10, False),1)=-1$

$Q((12, 10, False),1)=-1$

episode 2 
[{'state': (21, 10, False), 'reward': 1, 'action': 0}, {'state': (17, 10, False), 'reward': 0, 'action': 1}, {'state': (12, 10, False), 'reward': 0.0, 'action': 1}]

$Q((21, 10, False),0)=1$

$Q((17, 10, False),1)=0$

$Q((12, 10, False),1)=0$

For $Q((17, 10, False),1)$ and $Q((12, 10, False),1)$ is the average return 
i.e -1 for the first episode and 1 for the second.